System and method for predicting patient health within a patient management system

ABSTRACT

Systems and Methods for predicting patient health and patient relative well-being within a patient management system are disclosed. A preferred embodiment utilizes an implantable medical device comprising an analysis component and a sensing component further comprising a three-dimensional accelerometer, a transthoracic impedance sensor, a cardio-activity sensor, an oxygen saturation sensor and a blood glucose sensor. Some embodiments of a system disclosed herein also can be configured as an Advanced Patient Management System that helps better monitor, predict and manage chronic diseases.

TECHNICAL FIELD

[0001] The present system relates generally to a Patient ManagementSystem and particularly, but not by way of limitation, to such a systemthat can determine patient health, relative well-being and predictivedegradation by using the sensing functions of an implantable medicaldevice and analyzing the sensed patient data to predict patient health.

BACKGROUND

[0002] Implantable medical devices are becoming increasingly versatileand able to perform many different physiological sensing functions thatenable a clinician to quickly and accurately assess patient health.Traditionally, an accurate assessment of patient health required theclinician to synthesize often divergent or seemingly unrelatedindications of patient health. For example, a diagnosis of congestiveheart failure might include not only an assessment and evaluation ofcardiac function data, but also an evaluation of other physiologicalfactors like patient fatigue or respiration data.

[0003] Typically, a clinician will assess patient health by inquiringhow the patient feels or asking about the patient's activities and thenmake an indirect assessment based on the patient's response and theclinician's observation of the patient's appearance. However, thesemeasures are very subjective and are limited to the time of thepatient/clinician interaction and the quality of patient recall orwillingness to divulge information. These factors affect the quality ofthe assessment.

[0004] Modern implantable medical devices offer objective data to helpthe clinician assess patient health. Modern medical devices can senseand analyze physiological factors with improved accuracy and report thatsensed and analyzed information to the clinician or the patient. Thedata or information that a medical device reports in the form of asensed physiological parameter can be characterized as either derived ornon-derived data. Non-derived data can be understood as raw biometricinformation sensed by the medical device that has not been processed toany meaningful degree. For example, non-derived biometric informationmay comprise the quantified measurement of a patient's heart rate orblood pressure. In contrast, derived data is biometric information thathas been analyzed and perhaps assigned some qualitative or quantitativevalue. For example, as a medical device senses a patient's cardiac cycleand clinically analyzes that information, the medical device may reportthat an arrhythmia has occurred as the result of sensing and analyzing acardiac rhythm outside expected parameters. Other derived sensors mayinclude, the cumulative calories burned by daily activity, a weight lossmonitor, a participation in activities monitor, a depression monitor ordetermining the onset of cancer, all of which may be ascertained bysensing physiological data and analyzing that data by using clinicallyderived algorithms or other analytical tools.

[0005] An example of a sensor component of a medical device is anaccelerometer. An accelerometer is essentially a device capable ofmeasuring an object's relative orientation in a gravity field. It candirectly sense patient movement (non-derived data) and present thatinformation for analysis and perform as a derived sensor. Such derivedinformation might include whether a patient is fatigued by reason ofillness or because of overexertion. Thus, relative activity maycorrespond to relative patient health. In addition to simply determiningwhether a patient is ambulatory, a sensitive or finely-tunedaccelerometer can also determine a patieht's relative position, i.e.,whether the patient is sitting, standing, sleeping or distinguishwhether the patient is prone because he decided to lie down instead ofabruptly falling down. A sensitive accelerometer can also detect finebody movement, like the physical reflexes of a person coughing orsneezing.

[0006] Coughing is often more than an indication of a respiratoryirritation or condition like asthma or the onset of the common cold, butmay also be a common side effect of certain drugs. For example,Angiotensin Converting Enzyme (“ACE”) inhibitors may cause a patient tocough when the patient's dosage is too high. Thus, coughing may be usedto titrate the appropriate dose of a drug like an ACE inhibitor.

[0007] Implantable medical devices comprising cardio-sensors, i.e.,pacemakers, can also monitor and sense a patient's cardiac activity andprovide remedial therapy. In addition, such medical devices can senseand measure transthoracic impedance as a means to evaluate patientrespiration data.

[0008] As a measurement of respiration, modern implantable medicaldevices often employ a sensor that measures transthoracic impedance.Transthoracic impedance is essentially the measure of a voltage acrosssome known spacing or distance. To measure this voltage, the medicaldevice drives a current from the device to the tip of a lead and voltageis measured from another area proximate to the device and another areaproximate to the lead. For example, as a person's heart pumps, thetransthoracic impedance changes because the heart is moving relative tothe implanted device. Similarly, as a person's lung inflates anddeflates as he breathes, the geometry of the current flowing between thedevice and the tip of the lead changes. In measuring respiration, thespacing or distance is situated in such a way that the distance crossesover either a person's left or right lung. Thus, when the geometrychanges, the resistance also changes. In the context of breathing, theperiodicity of the resistance also can serve as an indication of therelative depth or shallowness of breathing. In other words, atransthoracic impedance sensor can determine the symmetricalrelationship between inhalation and exhalation. The symmetry ofinhalation to exhalation can establish a pattern of respiration that mayhave clinical meaning, like determining asthma, apnea or chronicobstructive pulmonary disease (“COPD”). Within the context of detectingan asthma attack, a symmetrical breathing pattern recognized by atransthoracic impedance monitor may comprise the forced expiratoryvolume over one second (“FEV1”). Modern medical devices that measuretransthoracic impedance can be configured to filter out the cardiaccomponent and other impedance noise and concentrate on measuring thebreathing component.

[0009] An implantable medical device may also employ a sensor thatmeasures blood glucose levels. In this way, the medical device maypredict the need for insulin therapy before the patient or clinicianobserves acute symptoms of hyperglycemia.

[0010] However, the data sensed by modern implantable medical devices isoften presented in a form that merely reduces the data to some numericalor relative value that requires the clinician to further analyze thenumerical or relative value output to make a meaningful clinicalassessment. In addition, current implantable medical devices frequentlyare not analytically robust enough to provide meaningful diagnosticassessments or predictions of patient health beyond the mere reportingof physiological data. Merely reporting physiological data can be oflimited value due to a person's natural ability to initially compensatefor nascent changes in health status. Because of such analytical andperceptual limitations, sensing cardiac activity or transthoracicimpedance data through a single implantable medical device may onlyprovide the clinician with a useful starting point for further clinicalanalysis.

[0011] Thus, for these and other reasons, there is a need for a PatientManagement System comprising an implantable medical device furthercomprising various physiological sensors that sense and report patientdata. The system is further adapted to analyze the sensed data in amanner that yields an accurate assessment or prediction of patienthealth or relative well-being. In this way, the system can be configuredto not only report a relative state of patient health and detect earlystage disease progression, but also alert the clinician to patienthealth degradation before the onset of an acute episode or symptomaticillness.

SUMMARY

[0012] According to one aspect of the invention, there is provided asystem and method for predicting patient health and relative well-beingwithin a Patient Management System using an implantable medical deviceconfigured with multiple physiological sensors in communication withother components of the system via a communications network.

[0013] The Patient Management System further includes an analyticalcomponent contained within the medical device or outside the device or acombination of internal and external analytical components. Anon-limiting example of such an analytical component is anexternally-based Advanced Patient Management System. As used herein,“physiological function data,” “physiology data,” “patient data” and“patient health data” are substantively synonymous terms and relate to ameasurable or relative physiological parameter. In addition to physicalparameters like heart rate, respiration and blood chemistry,physiological parameters may include, for example, subjectiveevaluations of well-being, perceived emotional state and otherpsychological attributes. Also, as used herein, a “clinician” can be aphysician, physician assistant (PA), nurse, medical technologist, or anyother patient health care provider.

[0014] In one embodiment of a system for predicting patient health andrelative well-being within a patient management system, the systemcomprises a medical device further comprising a sensing component, ananalysis component and a communications component. The sensing componentincludes one or more base sensors adapted to sense physiologicalfunction data. The analysis component is adapted to analyzephysiological data sensed by the sensing component and detect subtle,early indications of changes in disease state. The communicationscomponent is adapted to communicate sensed and analyzed physiologicaldata to the components of the system.

[0015] In another embodiment of the system for predicting patient healthand relative well-being within a patient management system, the medicaldevice comprising sensing, analysis and communications components isimplanted within a patient, and the sensing component includes anaccelerometer. The accelerometer can be configured to detect a patient'sfine and gross body motion, and can be a one-, two- or three-dimensionalaccelerometer. Example analysis includes detecting changes in measuredaccelerometer patterns that are indicative of early occurrence of a newdisease state or onset of illness or indicate progression of a disease.

[0016] In a further embodiment of the system for predicting patienthealth and relative well-being within a patient management system, thesensing component of the implantable medical device comprises anaccelerometer and a transthoracic impedance sensor. In this embodiment,the implantable medical device is adapted to detect a patient's fine andgross body motion and respiration parameters. Example analysis includesdetecting changes in transthoracic impedance variation patterns that areindicative of early occurrence of a new disease state (such as COPD) oronset of illness (such as asthma) or indicate progression of a disease(such as DC impedance indicating lung fluid accumulation whichcorresponds to progression of heart failure). Further, the sensed datacan be used in combination to cross-validate sensed conclusions, such asa change in accelerometer data pattern coincident withinhalation/exhalation time ratio measured by transthoracic impedance toindicate progression of asthma.

[0017] In yet another embodiment of the system for predicting patienthealth and relative well-being within a patient management system, thesensing component of the implantable medical device comprises anaccelerometer, a transthoracic impedance sensor and a cardio-activitysensor. In this embodiment, the implantable medical device is adapted todetect a patient's fine and gross body motion, respiration parameters,and cardiac-activity parameters. Example analysis includes monitoringleft and right intracardial R-wave amplitude and either singly reportingchanges or correlating changes with changes in accelerometer andtransthoracic impedance to form an early and confident indication ofonset of pulmonary edema.

[0018] In yet a further embodiment of the system for predicting patienthealth and relative well-being within a patient management system, thesensing component of the implantable medical device comprises anaccelerometer, a transthoracic impedance sensor, and an oxygensaturation sensor. In this embodiment, the implantable medical device isadapted to detect a patient's fine and gross body motion, respirationparameters, cardiac-activity parameters and blood gas data. Exampleanalysis includes combining changes in accelerometer and transthoracicimpedance with blood oxygen saturation to form an early and confidentindication of onset or progression of pulmonary edema.

[0019] In a preferred embodiment of the system for predicting patienthealth and relative well-being within a patient management system, thesensing component of the implantable medical device comprises athree-dimensional accelerometer, a transthoracic impedance sensor, acardio-activity sensor, an oxygen saturation sensor and a blood glucosesensor. In this embodiment, the implantable medical device is adapted todetect a patient's fine and gross body motion, respiration parameters,cardiac-activity parameters, blood gas data and episodes of hyper- andhypoglycemia. Example analysis includes combining changes inaccelerometer data, transthoracic impedance, blood oxygen saturation,cardio-activity and blood glucose for an early and confident indicationof onset and changes in cardiac and pulmonary disease states.

[0020] By selecting other base sensors, early and confident indicationsof onset or progression of diseases beyond cardiopulmonary can be made.By way of non-limiting example only, other base sensors might include acardiac output/ejection fraction sensor; a chamber pressure sensor; atemperature sensor; sodium, potassium, calcium and magnesium sensors; apH sensor; a partial oxygen sensor; a partial CO2 sensor; a cholesteroland triglyceride sensor; a catecholamine sensor; a creatinephosphokinase sensor; a lactate dehydrogenase sensor; a troponin sensor;a prothrombin time sensor; a complete blood count sensor; a blood ureanitrogen sensor; a body weight sensor; a blood (systemic) pressuresensor; a adrenocorticotropic hormone sensor; a thyroid marker sensor; agastric marker sensor and a creatinine sensor. Data from these sensorscan be analyzed to predict or detect, by way of non-limiting exampleonly, the early onset of stroke, pain quantification/determination,chronic depression, cancer tissue (onset, progression, recurrence),syncope, autonomic tone, myocardial infarct, ischemia and seizure.

[0021] The various embodiments described above are provided by way ofillustration only and should not be construed to limit the invention.Those skilled in the art will readily recognize various modificationsand changes that may be made to the present invention without followingthe example embodiments and applications illustrated and describedherein, and without departing from the true spirit and scope of thepresent invention, which is set forth in the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In the drawings, which are not necessarily drawn to scale, likenumerals describe substantially similar components throughout theseveral views. Like numerals having different letter suffixes representdifferent instances of substantially similar components. The drawingsillustrate generally, by way of example, but not by way of limitation,various embodiments discussed in the present document.

[0023]FIG. 1 is a schematic/block diagram illustrating generally, amongother things, one embodiment of the system and method for predictingpatient health within a patient management system.

[0024]FIG. 2 is a schematic/block diagram illustrating generally, amongother things, another embodiment of the system and method for predictingpatient health within a patient management system comprising anaccelerometer.

[0025]FIG. 3 is a schematic/block diagram illustrating generally, amongother things, another embodiment of the system and method for predictingpatient health within a patient management system comprising anaccelerometer and a transthoracic impedance sensor.

[0026]FIG. 4 is a schematic/block diagram illustrating generally, amongother things, another embodiment of the system and method for predictingpatient health within a patient management system comprising anaccelerometer, a transthoracic impedance sensor and an oxygen saturationsensor.

[0027]FIG. 5 is a schematic/block diagram illustrating generally, amongother things, another embodiment of the system and method for predictingpatient health within a patient management system comprising anaccelerometer, a transthoracic impedance sensor, an oxygen saturationsensor and a cardio-activity sensor.

[0028]FIG. 6 is a schematic/block diagram illustrating generally, amongother things, another embodiment of the system and method for predictingpatient health within a patient management system comprising anaccelerometer, a transthoracic impedance sensor, an oxygen saturationsensor, a cardio-activity sensor and a blood glucose sensor.

[0029]FIG. 7 is a schematic/block diagram illustrating generally, amongother things, another embodiment of the system and method for predictingpatient health within an Advanced Patient Management system.

[0030]FIG. 8 is a flow diagram illustrating generally, among otherthings, the interactive functions of the system and method forpredicting patient health within a patient management system.

DETAILED DESCRIPTION

[0031] In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which are shown byway of illustration, specific embodiments or examples. These embodimentsmay be combined, other embodiments may be utilized, and structural,logical and electrical changes may be made without departing from thespirit and scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope of the present invention is defined by the appended claims andtheir equivalents.

[0032] The present system and method are described with respect to animplantable medical device as a component of a Patient Management Systemcapable of predicting patient health and relative well-being by thecomprehensively analyzing sensed physiological data.

[0033]FIG. 1 is a schematic/block diagram illustrating generally anembodiment of the system and method for predicting patient health andrelative well-being within a patient management system 100. The systemcomprises a medical device further comprising a sensing component 101,an analysis component 102 and a communications component 103. Themedical device can be implantable 104 within a patient 105.

[0034] The sensing component 101 includes one or more sensors adapted tosense physiological data. The sensors may comprise an accelerometer, atransthoracic impedance sensor, an oxygen saturation sensor, and acardio-activity sensor.

[0035] The analysis component 102 is adapted to analyze physiologicaldata sensed by the sensing component. Analysis may be internal and/orexternal to the patient. Analysis may include the use of clinicallyderived algorithms to analyze the biometric data in a way that yields aclinically relevant output. The algorithms can be the result of theextraction, codification and use of collected expert knowledge for theanalysis or diagnosis of medical conditions. For example, the algorithmscan comprise institutional analytical or diagnostic techniques used inspecific clinical settings. By reducing the analytical or diagnosticmethodologies of institutions like the Cleveland Clinic, the Mayo Clinicor the Kaiser Permanente system to algorithmic expression, a patientwill enjoy the benefit of the medical expertise of a leading medicalinstitution without having to visit the institution. The analysis andsensing components are further adapted to electronically communicatewith the communications component.

[0036] The communications component 103 is adapted to communicate sensedand analyzed physiological data to the components of the system, whetherthe components are internal or external to the patient.

[0037]FIG. 2 is a schematic/block diagram illustrating generally anembodiment of the accelerometer 200 component of the system and methodfor predicting patient health and relative well-being within a patientmanagement system. The accelerometer 200 can be configured to detect apatient's fine and gross body motion. A suitable accelerometer includesa one-dimensional, two-dimensional 200 or three-dimensionalaccelerometer. Typically, a one-dimensional accelerometer only measuresmovement along a single axis 201 as further illustrated in FIG. 2. Atwo-dimensional accelerometer typically measures movement along twoorthogonal axes 202. A three-dimensional accelerometer measures movementalong three orthogonal axes 203. When the system comprises athree-dimensional accelerometer, the system can determine person's bodyposition with greatest accuracy. Thus, in addition to detecting grossbody movement, a sensitive accelerometer may be adapted to detect finebody movement, like a person coughing. When the system is configured toanalyze accelerometer data to determine whether a person is coughing, aclinician can utilize that derivative information two assist indetermining the onset of a common cold, influenza or the proper dosageof a drug, like an ACE inhibitor, that may cause a coughing side effectwhen the dosage is too high. In addition, coughing or other activitysensed by the accelerometer 200 may be used to titrate the dosage ofother drugs as a component of a near-term drug delivery system, whereinthe titration analysis is communicated to the patient or the clinician.

[0038]FIG. 3 is a schematic/block diagram illustrating generally anembodiment of the transthoracic impedance sensor 300 component of thesystem and method for predicting patient health and relative well-beingwithin a patient management system. In one embodiment, as illustrated inFIG. 3, the transthoracic impedance sensor 300 is a component of animplantable medical device 301. In this embodiment, the implantablemedical device comprises an accelerometer 200 as illustrated in FIG. 2and a transthoracic impedance sensor 300. A transthoracic impedancesensor 300 may be adapted to sense impedance changes in the heart orlungs or both. The transthoracic impedance sensor can be configured tofilter out the cardiac component and other impedance noise and focus onrespiration measurement. In such a filtered embodiment, thetransthoracic impedance sensor 300 can assist the clinician inpredicting the onset or presence of an asthma attack, apnea, COPD andFEV1. Further, in this embodiment, the transthoracic impedance sensor300 may also be configured to detect the accumulation of fluid in thelungs. Such detection may also serve to predictively indicate the onsetor existence of pulmonary disease.

[0039]FIG. 4 is a schematic/block diagram illustrating generally anembodiment of the oxygen saturation sensor 400 component of the systemand method for predicting patient health and relative well-being withina patient management system. In one embodiment, as illustrated in FIG.4, the oxygen saturation sensor 400 is a component of an implantablemedical device 301. In this embodiment, the implantable medical devicecomprises an accelerometer 200 as illustrated in FIG. 2, a transthoracicimpedance sensor 300 and an oxygen saturation sensor 400. An oxygensaturation sensor 400 determines the ratio between the deoxygenatedhemoglobin and oxygenated hemoglobin. In a healthy person, breathing airat sea level, the level of saturation is between 96% and, 98%. Abnormallevels may indicate a respiratory or environmental problem. Whencombined with other measurements of patient health, a patient's oxygensaturation level may provide further evidence of patient health orrelative well-being.

[0040]FIG. 5 is a schematic/block diagram illustrating generally anembodiment of the cardio-activity sensor 500 component of the system andmethod for predicting patient health and relative well-being within apatient management system. In one embodiment, as illustrated in FIG. 5,the cardio-activity sensor 500 is a component of an implantable medicaldevice 301. In this embodiment, the implantable medical device comprisesan accelerometer 200 as illustrated in FIG. 2, a transthoracic impedancesensor 300, an oxygen saturation sensor 400, and a cardio-activitysensor 500. The cardio-activity sensor 500 may be configured to detectcardiac arrhythmias. Depending on the nature of the arrhythmia, thecardio-activity sensor 500 may cause therapy to be directed to thepatient in the form of a low energy electrical stimuli, i.e., pacepulse, or a defibrillation countershock. The cardio-activity sensor 500may also be used to signal a clinician that an arrhythmia requiresfurther analysis or medical intervention. The cardio-activity sensor 500in this embodiment may also assist in predicting stroke by measuringST-segment changes in an electrocardiogram and conveying thatinformation to the analysis component 102 to confirm ST-segmentelevations or abnormalities.

[0041]FIG. 6 is a schematic/block diagram illustrating generally anembodiment of the blood glucose sensor 600 component of the system andmethod for predicting patient health and relative well-being within apatient management system. In one embodiment, as illustrated in FIG. 6,the blood glucose sensor 600 is a component of an implantable medicaldevice 301. In this embodiment, the implantable medical device comprisesan accelerometer 200 as illustrated in FIG. 2, a transthoracic impedancesensor 300, an oxygen saturation sensor 400, a cardio-activity sensor500 and a blood glucose sensor 600. The blood glucose sensor 600 may beconfigured to detect elevations or de-elevations in blood glucose.Depending on the nature of the blood glucose level, the blood glucosesensor 600 may cause therapy to be directed to the patient in the formof insulin administration or be used to signal an alert to the patientor clinician.

[0042]FIG. 7 is a schematic/block diagram illustrating generally anembodiment of the system and method for predicting patient health andrelative well-being within a patient management system 100 illustratingthe analysis of patient data by an externally-based Advanced PatientManagement System (“APM”) 700.

[0043] APM is a system that helps patients, their physicians and theirfamilies to better monitor, predict and manage chronic diseases. In theembodiment shown in FIG. 7, the APM system 700 consists of three primarycomponents: 1) an implantable medical device 301 with sensors adapted tomonitor physiological data, 2) a Data Management System (“DMS”) 701,adapted to process and store patient data 701 a collected from thesensors, patient population data 701 b, medical practice data 601 cfurther comprising clinically derived algorithms, and general practicedata 701 d, and 3) an analytical engine 702 adapted to analyze data fromthe DMS. APM is designed to support physicians and other clinicians inusing a variety of different devices, patient-specific and non-specificdata, along with medication therapy, to provide the best possible careto patients. Currently, implanted devices often provide only limitedsensing, analysis and therapy to patients. APM moves the device from areactive mode into a predictive one that allows a clinician to use APMto predict patient health.

[0044]FIG. 8 is a flow diagram illustrating generally the interactivefunctions of the system and method for predicting patient health andrelative well-being within a patient management system 100. Asillustrated in FIG. 8, the sensing 800, analysis 701 and communications802 components are interactive, thus allowing the components tocommunicate and share data. By way of non-limiting example only, thesensing component 800 would first sense physiological function data froma patient 105. The sensing component 800 may be further adapted toprovide therapy to the patient 105. That data would then be transmittedto analysis component 801 for analysis. Analysis may comprise the use ofclinically derived algorithms and may be performed internal and/orexternal to the patient 105. Based on the analysis, the sensingcomponent 800 may be further adapted to provide therapy to the patient105. The analyzed data is then received by communications module 802,which reports the analyzed data in the form of a determination ofpatient health or relative well-being to a patient 105 or clinician 105a. The communications component 802 may also be in communication with apatient management system, including an externally based AdvancedPatient Management system 803. Communication can be in the form of wiredor wireless electronic communication.

[0045] The various embodiments described above are provided by way ofillustration only and should not be construed to limit the invention.Those skilled in the art will readily recognize various modificationsand changes that may be made to the present invention without followingthe example embodiments and applications illustrated and describedherein, and without departing from the true spirit and scope of thepresent invention, which is set forth in the following claims.

[0046] It is to be understood that the above description is intended tobe illustrative, and not restrictive. For example, the above-describedembodiments may be used in combination with each other. Many otherembodiments will be apparent to those of skill in the art upon reviewingthe above description. The scope of the invention should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled. In the appendedclaims, the terms “including,” “includes” and “in which” are used as theplain-English equivalents of the respective terms “comprising,”“comprises” and “wherein.”

What is claimed is:
 1. A system for predicting patient health andwell-being within a patient management system comprising a medicaldevice further comprising: a. a sensing component in electroniccommunication with other components of the system including one or moresensors adapted to sense physiological function data; b. an analysiscomponent in electronic communication with other components of thesystem adapted to analyze the sensed physiological data; and c. acommunications component in electronic communication with othercomponents of the system adapted to communicate the sensed or analyzedphysiological data.
 2. The medical device of claim 1, wherein the deviceis an implantable medical device.
 3. The sensing component of claim 1,wherein the sensing component comprises an accelerometer.
 4. Theaccelerometer of claim 3, wherein the accelerometer comprises aone-dimensional accelerometer.
 5. The accelerometer of claim 3, whereinthe accelerometer comprises a two-dimensional accelerometer.
 6. Theaccelerometer of claim 3, wherein the accelerometer comprises athree-dimensional accelerometer.
 7. The sensing component of claim 1,wherein the sensing component comprises a transthoracic impedancesensor.
 8. The sensing component of claim 1, wherein the sensingcomponent comprises a cardio-activity sensor.
 9. The sensing componentof claim 1, wherein the sensing component comprises an oxygen saturationsensor.
 10. The sensing component of claim 1, wherein the sensingcomponent comprises a blood glucose sensor.
 11. The sensing component ofclaim 1, wherein the sensing component comprises a cardiacoutput/ejection fraction sensor.
 12. The sensing component of claim 1,wherein the sensing component comprises a chamber pressure sensor. 13.The sensing component of claim 1, wherein the sensing componentcomprises a temperature sensor.
 14. The sensing component of claim 1,wherein the sensing component comprises a sodium sensor.
 15. The sensingcomponent of claim 1, wherein the sensing component comprises apotassium sensor.
 16. The sensing component of claim 1, wherein thesensing component comprises a calcium sensor.
 17. The sensing componentof claim 1, wherein the sensing component comprises a magnesium sensor.18. The sensing component of claim 1, wherein the sensing componentcomprises a pH sensor.
 19. The sensing component of claim 1, wherein thesensing component comprises a partial oxygen sensor.
 20. The sensingcomponent of claim 1, wherein the sensing component comprises a partialCO2 sensor.
 21. The sensing component of claim 1, wherein the sensingcomponent comprises a cholesterol sensor.
 22. The sensing component ofclaim 1, wherein the sensing component comprises a triglyceride sensor.23. The sensing component of claim 1, wherein the sensing componentcomprises a catecholamine sensor.
 24. The sensing component of claim 1,wherein the sensing component comprises a creatine phosphokinase sensor.25. The sensing component of claim 1, wherein the sensing componentcomprises a lactate dehydrogenase sensor.
 26. The sensing component ofclaim 1, wherein the sensing component comprises a troponin sensor. 27.The sensing component of claim 1, wherein the sensing componentcomprises a prothrombin time sensor.
 28. The sensing component of claim1, wherein the sensing component comprises a complete blood countsensor.
 29. The sensing component of claim 1, wherein the sensingcomponent comprises a blood urea nitrogen sensor.
 30. The sensingcomponent of claim 1, wherein the sensing component comprises a bodyweight sensor.
 31. The sensing component of claim 1, wherein the sensingcomponent comprises a blood (systemic) pressure sensor.
 32. The sensingcomponent of claim 1, wherein the sensing component comprises aadrenocorticotropic hormone sensor.
 33. The sensing component of claim1, wherein the sensing component comprises a thyroid marker sensor. 34.The sensing component of claim 1, wherein the sensing componentcomprises a gastric marker sensor.
 35. The sensing component of claim 1,wherein the sensing component comprises a creatinine sensor.
 36. Theaccelerometer of claim 3, wherein the accelerometer is adapted to sensethe fine and gross body position of a person.
 37. The fine and grossbody position of claim 36, wherein the sensed body position of theperson comprises standing, sitting, lying on the back, lying on thestomach, lying upon the left side and lying on the right side.
 38. Theaccelerometer of claim 3, wherein the accelerometer is adapted to sensethe fine and gross body motion of a person.
 39. The fine and gross bodymotion of claim 38, wherein the sensed body motion comprises a baselinemeasurement of patient activity.
 40. The fine and gross body motion ofclaim 38, wherein the sensed body motion comprises a measure ofwell-being.
 41. The fine and gross body motion of claim 38, wherein thesensed body motion comprises a measure of lethargy.
 42. The measure oflethargy of claim 41, wherein the measure comprises the magnitude ofactivity and the frequency of activity.
 43. The accelerometer of claim3, wherein the accelerometer is adapted to detect a cough.
 44. Thedetected cough of claim 43, wherein the cough is analyzed to detect theonset of a common cold.
 45. The detected cough of claim 43, wherein thecough is analyzed to detect the onset of influenza.
 46. The detectedcough of claim 43, wherein the cough is analyzed to titrate a drug. 47.The titrated drug of claim 46, wherein the drug is an angiotensinconverting enzyme inhibitor.
 48. The titrated drug of claim 46, whereinthe drug comprises a near-term drug delivery system.
 49. The near-termdrug delivery system of claim 48, wherein the system comprisescommunication with a clinician.
 50. The near-term drug delivery systemof claim 48, wherein the system comprises communication with a patient.51. The analysis component of claim 1, wherein the analysis is performedinternal to the patient.
 52. The analysis component of claim 1, whereinthe analysis is performed external to the patient.
 53. The analysiscomponent of claim 1, wherein the analysis is performed, in part,internal to the patient.
 54. The analysis component of claim 1, whereinthe analysis is performed, in part, external to the patient.
 55. Theanalysis component of claim 1, wherein the analysis includes detectingchanges in sensed data patterns that are indicative of early occurrenceof a new disease state.
 56. The analysis component of claim 1, whereinthe analysis includes detecting changes in sensed data patterns that areindicative of onset of illness.
 57. The analysis component of claim 1,wherein the analysis includes detecting changes in sensed data patternsthat are indicative of progression of a disease.
 58. The analysiscomponent of claim 1, wherein the analysis includes detecting changes insensed accelerometer patterns that are indicative of early occurrence ofa new disease state.
 59. The analysis component of claim 1, wherein theanalysis includes detecting changes in sensed accelerometer patternsthat are indicative of onset of illness.
 60. The analysis component ofclaim 1, wherein the analysis includes detecting changes in sensedaccelerometer patterns that are indicative of progression of a disease.61. The analysis component of claim 1, wherein the analysis includesdetecting changes in transthoracic impedance variation patterns that areindicative of early occurrence of a new disease state.
 62. The newdisease state of claim 61, wherein the new disease state is chronicobstructive pulmonary disease.
 63. The analysis component of claim 1,wherein the analysis includes detecting changes in transthoracicimpedance variation patterns that are indicative of onset of illness.64. The onset of illness of claim 63, wherein the illness comprisesasthma.
 65. The analysis component of claim 1, wherein the analysisincludes detecting changes in transthoracic impedance variation patternsthat indicate progression of a disease.
 66. The progression of diseaseof claim 65, wherein the disease comprises heart failure.
 67. Theanalysis component of claim 1, wherein the analysis includes combiningsensed data to cross-validate sensed conclusions.
 68. The combinedsensed data of claim 67, wherein the combined data includes a change inaccelerometer data pattern coincident with inhalation/exhalation timeratio measured by transthoracic impedance.
 69. The combined data ofclaim 68, wherein the combined data indicates progression of asthma. 70.The analysis component of claim 1, wherein the analysis includesmonitoring left and right intracardial R-wave amplitude.
 71. Theanalysis of claim 70, wherein the analysis includes singly reportingchanges.
 72. The analysis of claim 71, wherein the analysis comprises anearly and confident indication of onset of pulmonary edema.
 73. Theanalysis of claim 70, wherein the analysis includes correlating left andright intracardial R-wave amplitude data with accelerometer andtransthoracic impedance data.
 74. The analysis of claim 73, wherein theanalysis comprises an early and confident indication of onset ofpulmonary edema.
 75. The analysis component of claim 1, wherein theanalysis includes combining accelerometer, transthoracic impedance andblood oxygen saturation data to form an early and confident indicationof onset of pulmonary edema.
 76. The analysis component of claim 1,wherein the analysis includes combining accelerometer, transthoracicimpedance and blood oxygen saturation data to form an early andconfident indication of progression of pulmonary edema.
 77. The analysiscomponent of claim 1, wherein the analysis includes combiningaccelerometer, transthoracic impedance, blood oxygen saturation,cardio-activity and blood glucose data for an early and confidentindication of onset of cardiac and pulmonary disease states.
 78. Theanalysis component of claim 1, wherein the analysis includes combiningaccelerometer, transthoracic impedance, blood oxygen saturation,cardio-activity and blood glucose data for an early and confidentindication of changes in cardiac and pulmonary disease states.
 79. Theanalysis component of claim 1, wherein the analysis includes combiningdata from other base sensors for an early and confident indication ofonset of diseases other than cardio-pulmonary diseases.
 80. The analysiscomponent of claim 1, wherein the analysis includes combining data fromother sensors for an early and confident indication of progression ofdiseases other than cardiopulmonary diseases.
 81. The communicationscomponent of claim 1, wherein the communications are wired electroniccommunications.
 82. The communications component of claim 1, wherein thecommunications are wireless electronic communications.
 83. Thecommunications component of claim 1, wherein the communications are acombination of wired and wireless electronic communications.
 84. Amethod for predicting patient health and well-being within a patientmanagement system comprising a medical device comprising the steps of:a. sensing physiological function data with one or more sensorcomponents in electronic communication with other components of thesystem and adapted to sense such data; b. analyzing the sensedphysiological data with an analysis component in electroniccommunication with other components of the system and adapted to analyzethe sensed data; and c. communicating the sensed and analyzedphysiological data with a communications component in electroniccommunication with other components of the system and adapted tocommunicate the sensed and analyzed data to the components of thesystem.
 85. The method of claim 84, wherein the step of sensingphysiological function data comprises the further step of sensing a fineand gross body position of a person with an accelerometer.
 86. Themethod of claim 84, wherein the step of sensing physiological functiondata comprises the further step of sensing respiration function data ofa person with an transthoracic impedance sensor.
 87. The method of claim84, wherein the step of sensing physiological function data comprisesthe further step of sensing cardiac activity of a person with acardio-activity sensor.
 88. The method of claim 84, wherein the step ofsensing physiological function data comprises the further step of oxygensaturation of a person with an oxygen saturation sensor.
 89. The methodof claim 84, wherein the step of sensing physiological function datacomprises the further step of oxygen saturation of a person with a bloodglucose sensor.
 90. The method of claim 84, wherein the step ofanalyzing sensed physiological function data comprises the further stepof analyzing changes in sensed data patterns that are indicative ofearly occurrence of a new disease state.
 91. The method of claim 84,wherein the step of analyzing sensed physiological function datacomprises the further step of analyzing changes in sensed data patternsthat are indicative of onset of illness.
 92. The method of claim 84,wherein the step of analyzing sensed physiological function datacomprises the further step of analyzing changes in sensed data patternsthat are indicative of progression of a disease.
 93. The method of claim84, wherein the step of analyzing sensed physiological function datacomprises the further step of analyzing the sensed physiologicalfunction data by using clinically derived algorithms.
 94. The step ofanalyzing sensed physiological function data of claim 93, wherein thestep comprises the further step of analyzing the sensed physiologicaldata by using algorithms reflecting a standard of medical care of amedical institution.
 95. The method of claim 84, wherein the step ofanalyzing sensed physiological function data comprises the further stepof analyzing the sensed physiological function data with an AdvancedPatient Management system.
 96. The method of claim 84, wherein the stepof communicating the sensed and analyzed physiological function datacomprises the further step of electronically communicating the sensedand analyzed data to other components of the system.
 97. The method ofclaim 96, wherein the step of electronically communicating the sensedand analyzed physiological function data comprises the further step ofwirelessly communicating the sensed and analyzed data.
 98. The step ofcommunicating the sensed and analyzed physiological function data ofclaim 96, wherein the step comprises the further step of communicatingthe sensed and analyzed data to a patient management system.
 99. Thestep of communicating the sensed and analyzed physiological functiondata of claim 97, wherein the step comprises the further step ofcommunicating the sensed and analyzed data to a patient managementsystem.
 100. The step of communicating the sensed and analyzedphysiological function data of claim 96, wherein the step comprises thefurther step of communicating the sensed and analyzed data to anAdvanced Patient Management system.
 101. The step of communicating thesensed and analyzed physiological function data of claim 97, wherein thestep comprises the further step of communicating the sensed and analyzeddata to an Advanced Patient Management system.